To use incense we first have to instantiate an experiment loader that will enable us to query the database for specific runs.
| targets_type | iteration | autoencoder_type | batch_size | artifacts | |
|---|---|---|---|---|---|
| exp_id | |||||
| 42 | Mnist | False | nomal_dim_tied | 256 | {'history_autoencoder': Artifact(name=history_... |
| 43 | Mnist | False | nomal_dim_tied | 128 | {'history_autoencoder': Artifact(name=history_... |
| 44 | Mnist | False | nomal_dim_tied | 64 | {'history_autoencoder': Artifact(name=history_... |
| 45 | Mnist | False | nomal_dim_tied | 32 | {'history_autoencoder': Artifact(name=history_... |
| 46 | 10_Targets | False | nomal_dim_tied | 256 | {'history_autoencoder': Artifact(name=history_... |
| 47 | 10_Targets | False | nomal_dim_tied | 128 | {'history_autoencoder': Artifact(name=history_... |
| 48 | 10_Targets | False | nomal_dim_tied | 64 | {'history_autoencoder': Artifact(name=history_... |
| 49 | 10_Targets | False | nomal_dim_tied | 32 | {'history_autoencoder': Artifact(name=history_... |
| targets_type | iteration | autoencoder_type | batch_size | artifacts | sort | |
|---|---|---|---|---|---|---|
| exp_id | ||||||
| 46 | 10_Targets | False | nomal_dim_tied | 256 | {'history_autoencoder': Artifact(name=history_... | 0 |
| 47 | 10_Targets | False | nomal_dim_tied | 128 | {'history_autoencoder': Artifact(name=history_... | 1 |
| 48 | 10_Targets | False | nomal_dim_tied | 64 | {'history_autoencoder': Artifact(name=history_... | 2 |
| 49 | 10_Targets | False | nomal_dim_tied | 32 | {'history_autoencoder': Artifact(name=history_... | 3 |
| 42 | Mnist | False | nomal_dim_tied | 256 | {'history_autoencoder': Artifact(name=history_... | 4 |
| 43 | Mnist | False | nomal_dim_tied | 128 | {'history_autoencoder': Artifact(name=history_... | 5 |
| 44 | Mnist | False | nomal_dim_tied | 64 | {'history_autoencoder': Artifact(name=history_... | 6 |
| 45 | Mnist | False | nomal_dim_tied | 32 | {'history_autoencoder': Artifact(name=history_... | 7 |
Red best overall, and also best of subset. Bes means for accuracy max, rest min. Green best of subset.
predictions_df_0
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.9725 | 0.9742 | 0.9743 | 0.9775 | 0.9763 | 0.9744 | 0.9749 | 0.9748 |
| 1 | 0.9656 | 0.9711 | 0.9694 | 0.9743 | 0.9726 | 0.969 | 0.9703 | 0.9705 |
| 2 | 0.9615 | 0.9706 | 0.968 | 0.9737 | 0.9634 | 0.9603 | 0.958 | 0.9603 |
| 3 | 0.9577 | 0.9707 | 0.9679 | 0.9736 | 0.9486 | 0.9424 | 0.9417 | 0.945 |
| 4 | 0.9335 | 0.9707 | 0.9679 | 0.9735 | 0.9214 | 0.9215 | 0.9162 | 0.9194 |
| 5 | 0.8739 | 0.9707 | 0.9679 | 0.9735 | 0.8823 | 0.8894 | 0.8814 | 0.8878 |
| 6 | 0.8738 | 0.9707 | 0.9679 | 0.9735 | 0.8259 | 0.8357 | 0.8309 | 0.8459 |
| 7 | 0.8738 | 0.9707 | 0.9679 | 0.9735 | 0.7509 | 0.7709 | 0.782 | 0.7942 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.401906 | 0.402182 | 0.400775 | 0.404257 | 0.0351395 | 0.0356417 | 0.0361282 | 0.0350248 |
| 1 | 0.411027 | 0.408356 | 0.411532 | 0.41424 | 0.0506944 | 1.5165 | 0.049044 | 0.0497665 |
| 2 | 0.413155 | 0.40924 | 0.413617 | 0.415777 | 128.011 | 1.53466e+12 | 0.104961 | 0.0795622 |
| 3 | 0.413529 | 0.409625 | 0.414313 | 0.416035 | 5.74169e+12 | 1.73859e+24 | 5.99697e+11 | 7.6421e+10 |
| 4 | 0.414155 | 0.409752 | 0.414566 | 0.416082 | 2.57946e+23 | inf | 1.51443e+25 | 1.45618e+24 |
| 5 | 0.429525 | 0.409864 | 0.414699 | 0.416094 | inf | inf | inf | inf |
| 6 | 0.432744 | 0.409999 | 0.414776 | 0.416091 | inf | inf | inf | inf |
| 7 | 0.432623 | 0.410205 | 0.414882 | 0.416091 | inf | inf | inf | inf |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.267925 | 0.266109 | 0.266977 | 0.267262 | 0.0764037 | 0.0766775 | 0.0747766 | 0.072633 |
| 1 | 0.27 | 0.267454 | 0.269877 | 0.269511 | 0.092591 | 0.0982461 | 0.0874124 | 0.0866574 |
| 2 | 0.270812 | 0.267775 | 0.270718 | 0.269808 | 0.181491 | 8695.06 | 0.103686 | 0.103388 |
| 3 | 0.271383 | 0.267935 | 0.271334 | 0.269856 | 14576.1 | 9.2552e+09 | 10159 | 2789.89 |
| 4 | 0.272609 | 0.268017 | 0.271653 | 0.269869 | 3.08947e+09 | 9.85098e+15 | 5.10516e+10 | 1.21783e+10 |
| 5 | 0.279003 | 0.268097 | 0.271912 | 0.269872 | 6.54831e+14 | 1.04851e+22 | 2.56548e+17 | 5.31604e+16 |
| 6 | 0.279818 | 0.268191 | 0.27208 | 0.269871 | 1.38795e+20 | 1.11601e+28 | 1.28922e+24 | 2.32054e+23 |
| 7 | 0.279777 | 0.268331 | 0.27227 | 0.269871 | 2.94183e+25 | inf | 6.47866e+30 | 1.01296e+30 |
predictions_df_10
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.9394 | 0.9535 | 0.9579 | 0.9628 | 0.9302 | 0.9356 | 0.9158 | 0.8846 |
| 1 | 0.9246 | 0.9475 | 0.9511 | 0.9585 | 0.9145 | 0.9342 | 0.8716 | 0.8591 |
| 2 | 0.9152 | 0.9456 | 0.9498 | 0.9571 | 0.88 | 0.9124 | 0.8265 | 0.8258 |
| 3 | 0.9098 | 0.9452 | 0.9497 | 0.9566 | 0.8421 | 0.8821 | 0.7943 | 0.7916 |
| 4 | 0.8806 | 0.9451 | 0.9495 | 0.9567 | 0.7945 | 0.8462 | 0.7569 | 0.7536 |
| 5 | 0.8323 | 0.9451 | 0.9493 | 0.9567 | 0.7376 | 0.7956 | 0.7154 | 0.711 |
| 6 | 0.8321 | 0.945 | 0.9492 | 0.9567 | 0.6773 | 0.7381 | 0.6704 | 0.6628 |
| 7 | 0.8321 | 0.945 | 0.9492 | 0.9567 | 0.6038 | 0.6653 | 0.6266 | 0.6101 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.396474 | 0.399234 | 0.397118 | 0.400997 | 0.192351 | 0.110588 | 194.25 | 323.5 |
| 1 | 0.41353 | 0.40974 | 0.4129 | 0.414831 | 2.5013e+09 | 1.7332e+10 | 4.86995e+15 | 6.0761e+15 |
| 2 | 0.419493 | 0.41211 | 0.415773 | 16318.8 | 1.12371e+20 | 1.9635e+22 | 1.22982e+29 | 1.15778e+29 |
| 3 | 0.421175 | 1.01524 | 0.416629 | 8.81034e+17 | 5.04826e+30 | inf | inf | inf |
| 4 | 0.422485 | 2.05994e+13 | 0.41691 | inf | inf | inf | inf | inf |
| 5 | 0.436508 | 7.20358e+26 | 0.417071 | inf | inf | inf | inf | inf |
| 6 | 0.439568 | inf | 0.417155 | inf | inf | inf | nan | nan |
| 7 | 0.439455 | inf | 0.41726 | inf | inf | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.268623 | 0.266593 | 0.267358 | 0.26736 | 0.139373 | 0.117621 | 0.314835 | 0.441296 |
| 1 | 0.272837 | 0.268774 | 0.270971 | 0.270213 | 3132.26 | 4635.32 | 999073 | 1.50629e+06 |
| 2 | 0.274721 | 0.269433 | 0.271915 | 1.3168 | 6.63914e+08 | 4.93512e+09 | 5.02064e+12 | 6.57535e+12 |
| 3 | 0.275617 | 0.275556 | 0.272554 | 7.6888e+06 | 1.4072e+14 | 5.2528e+15 | 2.523e+19 | 2.87026e+19 |
| 4 | 0.277031 | 35701.6 | 0.272898 | 5.65003e+13 | 2.98264e+19 | 5.59094e+21 | 1.26787e+26 | 1.25292e+26 |
| 5 | 0.282812 | 2.11124e+11 | 0.273168 | 4.15186e+20 | 6.32187e+24 | 5.95084e+27 | inf | inf |
| 6 | 0.2836 | 1.24849e+18 | 0.273348 | 3.05095e+27 | 1.33995e+30 | inf | nan | nan |
| 7 | 0.28356 | 7.38298e+24 | 0.273539 | inf | inf | nan | nan | nan |
predictions_df_20
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.8895 | 0.9119 | 0.9304 | 0.9357 | 0.8785 | 0.8809 | 0.8246 | 0.7873 |
| 1 | 0.8638 | 0.9004 | 0.9137 | 0.9297 | 0.8418 | 0.8677 | 0.7476 | 0.7315 |
| 2 | 0.8499 | 0.8973 | 0.9107 | 0.9275 | 0.7846 | 0.8324 | 0.7059 | 0.6908 |
| 3 | 0.8427 | 0.8969 | 0.9101 | 0.9273 | 0.734 | 0.7962 | 0.6689 | 0.6575 |
| 4 | 0.8138 | 0.8969 | 0.9099 | 0.9272 | 0.6795 | 0.7513 | 0.6252 | 0.6249 |
| 5 | 0.7717 | 0.8969 | 0.9098 | 0.9273 | 0.6211 | 0.6896 | 0.5839 | 0.5854 |
| 6 | 0.7716 | 0.8969 | 0.9097 | 0.9273 | 0.5573 | 0.6217 | 0.5472 | 0.5458 |
| 7 | 0.7716 | 0.8969 | 0.9097 | 0.9273 | 0.4964 | 0.5511 | 0.5143 | 0.5007 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.393815 | 0.397695 | 0.393258 | 0.398758 | 0.540352 | 12.6282 | 2042.09 | 2093.66 |
| 1 | 0.421537 | 0.415857 | 0.416541 | 8.61158e+09 | 1.03755e+10 | 1.3519e+13 | 5.12708e+16 | 3.94084e+16 |
| 2 | 7012.26 | 0.41945 | 0.421664 | 4.65015e+23 | 4.66121e+20 | 1.53155e+25 | 1.29475e+30 | 7.50917e+29 |
| 3 | 1.37016e+16 | 0.420125 | 0.423078 | inf | 2.09405e+31 | inf | inf | inf |
| 4 | 2.678e+28 | 0.420328 | 0.423595 | inf | inf | inf | inf | inf |
| 5 | inf | 0.420459 | 0.42377 | inf | inf | inf | inf | inf |
| 6 | inf | 0.420585 | 0.423853 | nan | inf | inf | nan | nan |
| 7 | inf | 0.420775 | 0.424009 | nan | inf | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.271418 | 0.268266 | 0.268724 | 0.268546 | 0.215259 | 0.212319 | 1.53183 | 2.0569 |
| 1 | 0.278964 | 0.272681 | 0.273929 | 864.386 | 10558.3 | 73355.8 | 7.31266e+06 | 8.94264e+06 |
| 2 | 0.948803 | 0.273592 | 0.275314 | 6.35269e+09 | 2.23792e+09 | 7.80861e+10 | 3.67481e+13 | 3.90366e+13 |
| 3 | 932442 | 0.273795 | 0.276075 | 4.66821e+16 | 4.7434e+14 | 8.31127e+16 | 1.84669e+20 | 1.70402e+20 |
| 4 | 1.30359e+12 | 0.273888 | 0.276487 | 3.43038e+23 | 1.00539e+20 | 8.84628e+22 | 9.2801e+26 | 7.43833e+26 |
| 5 | 1.82247e+18 | 0.27397 | 0.276757 | 2.52078e+30 | 2.13098e+25 | 9.41574e+28 | inf | inf |
| 6 | 2.54788e+24 | 0.274059 | 0.27694 | nan | 4.51672e+30 | inf | nan | nan |
| 7 | 3.56204e+30 | 0.274189 | 0.277151 | nan | inf | nan | nan | nan |
predictions_df_30
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.8281 | 0.8623 | 0.8954 | 0.8891 | 0.8088 | 0.8186 | 0.7288 | 0.6939 |
| 1 | 0.7974 | 0.8449 | 0.8698 | 0.8832 | 0.7589 | 0.7919 | 0.6393 | 0.6139 |
| 2 | 0.7828 | 0.8425 | 0.8665 | 0.882 | 0.6936 | 0.7455 | 0.5955 | 0.5662 |
| 3 | 0.7724 | 0.8418 | 0.8658 | 0.8812 | 0.6349 | 0.7016 | 0.5589 | 0.5347 |
| 4 | 0.7455 | 0.8415 | 0.8657 | 0.8812 | 0.5734 | 0.6465 | 0.5258 | 0.5053 |
| 5 | 0.7108 | 0.8414 | 0.8659 | 0.8812 | 0.5129 | 0.5884 | 0.4932 | 0.4742 |
| 6 | 0.7104 | 0.8414 | 0.8658 | 0.8812 | 0.4573 | 0.5242 | 0.4514 | 0.4398 |
| 7 | 0.7105 | 0.8414 | 0.8658 | 0.8812 | 0.405 | 0.459 | 0.4225 | 0.4086 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.392332 | 0.396977 | 0.3898 | 0.39654 | 0.939852 | 163.789 | 25688.7 | 25963.6 |
| 1 | 4109.67 | 0.423185 | 104.969 | 1.37296e+10 | 2.00048e+10 | 1.82068e+14 | 6.47113e+17 | 4.92275e+17 |
| 2 | 8.02976e+15 | 110.179 | 3.22847e+14 | 7.4138e+23 | 8.98718e+20 | 2.06263e+26 | 1.63417e+31 | 9.38017e+30 |
| 3 | 1.56943e+28 | 3.82779e+15 | 9.98935e+26 | inf | 4.0375e+31 | inf | inf | inf |
| 4 | inf | 1.33857e+29 | inf | inf | inf | inf | inf | inf |
| 5 | inf | inf | inf | inf | inf | inf | inf | inf |
| 6 | inf | inf | inf | nan | inf | inf | nan | nan |
| 7 | nan | inf | nan | nan | inf | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.274316 | 0.270822 | 0.270462 | 0.270851 | 0.289015 | 0.526565 | 8.64217 | 11.1109 |
| 1 | 0.793936 | 0.277284 | 0.360978 | 976.664 | 17795.6 | 396072 | 4.38253e+07 | 4.98728e+07 |
| 2 | 713950 | 0.359235 | 146412 | 7.17728e+09 | 3.77193e+09 | 4.2159e+11 | 2.20234e+14 | 2.17705e+14 |
| 3 | 9.98131e+11 | 486670 | 2.57538e+11 | 5.27415e+16 | 7.99482e+14 | 4.48728e+17 | 1.10673e+21 | 9.50319e+20 |
| 4 | 1.39543e+18 | 2.87796e+12 | 4.53013e+17 | 3.87565e+23 | 1.69455e+20 | 4.77614e+23 | 5.56162e+27 | 4.14831e+27 |
| 5 | 1.95086e+24 | 1.70189e+19 | 7.96859e+23 | 2.84798e+30 | 3.59168e+25 | 5.08359e+29 | inf | inf |
| 6 | 2.72738e+30 | 1.00642e+26 | 1.40169e+30 | nan | 7.61276e+30 | inf | nan | nan |
| 7 | nan | inf | nan | nan | inf | nan | nan | nan |
predictions_df_40
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.7442 | 0.8027 | 0.842 | 0.8293 | 0.7398 | 0.7475 | 0.651 | 0.5991 |
| 1 | 0.7129 | 0.7811 | 0.816 | 0.8204 | 0.6803 | 0.6971 | 0.5404 | 0.4956 |
| 2 | 0.6968 | 0.7758 | 0.81 | 0.8196 | 0.6035 | 0.6439 | 0.4988 | 0.4599 |
| 3 | 0.6888 | 0.7751 | 0.8088 | 0.8193 | 0.5412 | 0.5942 | 0.4616 | 0.4346 |
| 4 | 0.667 | 0.775 | 0.8088 | 0.8193 | 0.4825 | 0.5403 | 0.4276 | 0.411 |
| 5 | 0.6364 | 0.7749 | 0.8085 | 0.8193 | 0.4268 | 0.4843 | 0.3953 | 0.3813 |
| 6 | 0.6364 | 0.7747 | 0.8085 | 0.8193 | 0.3764 | 0.4331 | 0.3618 | 0.3564 |
| 7 | 0.6364 | 0.7745 | 0.8085 | 0.8193 | 0.3273 | 0.3749 | 0.3398 | 0.3281 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.394308 | 0.399596 | 0.388105 | 0.398341 | 1.29555 | 2114.88 | 74397.2 | 66594.4 |
| 1 | 0.437422 | 2.05557e+09 | 0.42805 | 8.67514e+10 | 2.8314e+10 | 2.38535e+15 | 1.87499e+18 | 1.26391e+18 |
| 2 | 0.452615 | 7.18831e+22 | 0.43748 | 4.68447e+24 | 1.27201e+21 | 2.70234e+27 | 4.73496e+31 | 2.40835e+31 |
| 3 | 0.457902 | inf | 0.440222 | inf | 5.7145e+31 | inf | inf | inf |
| 4 | 0.460749 | inf | 0.440985 | inf | inf | inf | inf | inf |
| 5 | 0.469558 | inf | 0.441284 | inf | inf | inf | inf | inf |
| 6 | 0.471745 | nan | 0.441408 | nan | inf | nan | nan | nan |
| 7 | 0.471675 | nan | 0.441505 | nan | inf | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.278815 | 0.274847 | 0.273459 | 0.275008 | 0.353926 | 1.61756 | 19.856 | 20.8491 |
| 1 | 0.290441 | 356.868 | 0.28224 | 4578.13 | 23643.2 | 1.55248e+06 | 1.01494e+08 | 9.40415e+07 |
| 2 | 0.29404 | 2.10907e+09 | 0.284501 | 3.365e+10 | 5.01135e+09 | 1.65246e+12 | 5.10035e+14 | 4.1051e+14 |
| 3 | 0.295634 | 1.24721e+16 | 0.285462 | 2.47273e+17 | 1.06218e+15 | 1.75883e+18 | 2.56306e+21 | 1.79195e+21 |
| 4 | 0.297137 | 7.3754e+22 | 0.285961 | 1.81706e+24 | 2.25136e+20 | 1.87205e+24 | 1.28801e+28 | 7.82216e+27 |
| 5 | 0.300661 | 4.36146e+29 | 0.286263 | 1.33525e+31 | 4.77187e+25 | 1.99256e+30 | inf | inf |
| 6 | 0.30125 | nan | 0.286468 | nan | 1.01142e+31 | nan | nan | nan |
| 7 | 0.301221 | nan | 0.286654 | nan | inf | nan | nan | nan |
predictions_df_50
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.6727 | 0.7393 | 0.7898 | 0.7637 | 0.6826 | 0.6847 | 0.5667 | 0.5237 |
| 1 | 0.6395 | 0.7144 | 0.751 | 0.7551 | 0.6165 | 0.6183 | 0.4546 | 0.4094 |
| 2 | 0.6247 | 0.7101 | 0.7465 | 0.7542 | 0.5355 | 0.5602 | 0.4147 | 0.373 |
| 3 | 0.615 | 0.7091 | 0.7448 | 0.7535 | 0.4729 | 0.5022 | 0.3839 | 0.3522 |
| 4 | 0.5959 | 0.7091 | 0.7447 | 0.7538 | 0.4163 | 0.4438 | 0.3554 | 0.3317 |
| 5 | 0.5727 | 0.709 | 0.7445 | 0.754 | 0.3681 | 0.3906 | 0.3288 | 0.3155 |
| 6 | 0.5728 | 0.709 | 0.7445 | 0.754 | 0.3186 | 0.3431 | 0.2973 | 0.2883 |
| 7 | 0.5728 | 0.709 | 0.7445 | 0.754 | 0.2832 | 0.2905 | 0.2862 | 0.267 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.397009 | 0.402216 | 0.38788 | 1.75283 | 1.93966 | 7096.74 | 306678 | 174467 |
| 1 | 0.446041 | 8.81811e+09 | 2.59058e+08 | 7.16085e+13 | 4.72365e+10 | 8.01223e+15 | 7.73425e+18 | 3.31318e+18 |
| 2 | 0.462857 | 3.08368e+23 | 8.01559e+20 | 3.86677e+27 | 2.1221e+21 | 9.07697e+27 | 1.95315e+32 | 6.31319e+31 |
| 3 | 2.81401e+06 | inf | inf | inf | 9.53357e+31 | inf | inf | inf |
| 4 | 5.49996e+18 | inf | inf | inf | inf | inf | inf | inf |
| 5 | 1.07497e+31 | inf | inf | inf | inf | inf | inf | inf |
| 6 | inf | nan | inf | nan | inf | nan | nan | nan |
| 7 | inf | nan | nan | nan | inf | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.28291 | 0.278408 | 0.277079 | 0.289299 | 0.431472 | 4.12467 | 57.6568 | 47.0914 |
| 1 | 0.295947 | 954.831 | 223.894 | 80662.7 | 32253.2 | 4.28743e+06 | 2.94178e+08 | 2.11885e+08 |
| 2 | 0.299673 | 5.64561e+09 | 3.93557e+08 | 5.92768e+11 | 6.8363e+09 | 4.5635e+12 | 1.47832e+15 | 9.24921e+14 |
| 3 | 13.6885 | 3.33855e+16 | 6.92275e+14 | 4.35589e+18 | 1.44899e+15 | 4.85727e+18 | 7.42895e+21 | 4.03744e+21 |
| 4 | 1.87191e+07 | 1.97426e+23 | 1.21772e+21 | 3.20088e+25 | 3.07121e+20 | 5.16994e+24 | 3.73324e+28 | 1.76241e+28 |
| 5 | 2.61701e+13 | 1.16749e+30 | 2.142e+27 | inf | 6.5096e+25 | 5.50274e+30 | inf | inf |
| 6 | 3.65868e+19 | nan | inf | nan | 1.37975e+31 | nan | nan | nan |
| 7 | 5.11498e+25 | nan | nan | nan | inf | nan | nan | nan |
predictions_df_60
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.5972 | 0.6484 | 0.7067 | 0.6803 | 0.618 | 0.6188 | 0.4835 | 0.4652 |
| 1 | 0.567 | 0.6245 | 0.6715 | 0.669 | 0.5591 | 0.5465 | 0.3762 | 0.3439 |
| 2 | 0.5509 | 0.6193 | 0.664 | 0.6696 | 0.4645 | 0.4734 | 0.3379 | 0.3106 |
| 3 | 0.5437 | 0.6185 | 0.662 | 0.6697 | 0.3986 | 0.4095 | 0.3132 | 0.297 |
| 4 | 0.5286 | 0.618 | 0.6614 | 0.6696 | 0.3436 | 0.354 | 0.2875 | 0.2804 |
| 5 | 0.5065 | 0.6178 | 0.6612 | 0.6695 | 0.2996 | 0.3113 | 0.2661 | 0.2682 |
| 6 | 0.5062 | 0.6177 | 0.6612 | 0.6694 | 0.2618 | 0.2779 | 0.2432 | 0.2441 |
| 7 | 0.5062 | 0.6177 | 0.6612 | 0.6694 | 0.233 | 0.2331 | 0.2334 | 0.2242 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.400592 | 0.40445 | 0.389921 | 0.49054 | 3.93332 | 12739.7 | 537799 | 279889 |
| 1 | 9.71486 | 3.22605e+08 | 5.23878e+07 | 4.28895e+12 | 1.27987e+11 | 1.43873e+16 | 1.35649e+19 | 5.31527e+18 |
| 2 | 1.79304e+13 | 1.12814e+22 | 1.62094e+20 | 2.31598e+26 | 5.74982e+21 | 1.62991e+28 | 3.42557e+32 | 1.01281e+32 |
| 3 | 3.50451e+25 | inf | inf | inf | inf | inf | inf | inf |
| 4 | inf | inf | inf | inf | inf | inf | inf | inf |
| 5 | inf | inf | inf | inf | inf | inf | inf | inf |
| 6 | inf | nan | inf | nan | inf | nan | nan | nan |
| 7 | inf | nan | nan | nan | nan | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.287791 | 0.28281 | 0.28203 | 0.289423 | 0.497351 | 6.99165 | 91.9893 | 76.4692 |
| 1 | 0.326877 | 142.39 | 59.2327 | 31705.5 | 38315.7 | 7.40249e+06 | 4.6907e+08 | 3.43652e+08 |
| 2 | 34330.8 | 8.40639e+08 | 1.03744e+08 | 2.33014e+11 | 8.12128e+09 | 7.87913e+12 | 2.3572e+15 | 1.50011e+15 |
| 3 | 4.79957e+10 | 4.97115e+15 | 1.82488e+14 | 1.71227e+18 | 1.72135e+15 | 8.38632e+18 | 1.18455e+22 | 6.54823e+21 |
| 4 | 6.70999e+16 | 2.9397e+22 | 3.20999e+20 | 1.25825e+25 | 3.64849e+20 | 8.92617e+24 | 5.9527e+28 | 2.85842e+28 |
| 5 | 9.38082e+22 | 1.7384e+29 | 5.64643e+26 | 9.24608e+31 | 7.73318e+25 | 9.50077e+30 | inf | inf |
| 6 | 1.31148e+29 | nan | inf | nan | 1.63909e+31 | nan | nan | nan |
| 7 | inf | nan | nan | nan | nan | nan | nan | nan |
predictions_df_70
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.5177 | 0.5779 | 0.625 | 0.5847 | 0.5464 | 0.542 | 0.4177 | 0.4029 |
| 1 | 0.4944 | 0.5506 | 0.5885 | 0.5759 | 0.4801 | 0.4581 | 0.3161 | 0.2757 |
| 2 | 0.4792 | 0.5447 | 0.5811 | 0.5767 | 0.3929 | 0.3814 | 0.2818 | 0.2455 |
| 3 | 0.4713 | 0.5434 | 0.5796 | 0.5772 | 0.3357 | 0.3237 | 0.2576 | 0.2319 |
| 4 | 0.4584 | 0.5428 | 0.5794 | 0.5774 | 0.2908 | 0.2806 | 0.2363 | 0.2189 |
| 5 | 0.4424 | 0.5428 | 0.5791 | 0.5774 | 0.252 | 0.2471 | 0.2179 | 0.2105 |
| 6 | 0.4422 | 0.5427 | 0.5792 | 0.5773 | 0.2182 | 0.2228 | 0.2001 | 0.1908 |
| 7 | 0.4422 | 0.5427 | 0.5792 | 0.5772 | 0.1953 | 0.1811 | 0.1935 | 0.1827 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.407117 | 1.48135 | 0.405271 | 327.569 | 14.5561 | 42173 | 1.18157e+06 | 550661 |
| 1 | 0.466829 | 3.65775e+13 | 2.47455e+10 | 1.76437e+16 | 5.85676e+11 | 4.76768e+16 | 2.98094e+19 | 1.04641e+19 |
| 2 | 0.486444 | 1.27911e+27 | 7.6566e+22 | 9.5274e+29 | 2.63116e+22 | 5.40125e+28 | 7.52785e+32 | 1.9939e+32 |
| 3 | 281.186 | inf | inf | inf | inf | inf | inf | inf |
| 4 | 5.47993e+14 | inf | inf | inf | inf | inf | inf | inf |
| 5 | 1.07106e+27 | inf | inf | inf | inf | inf | inf | inf |
| 6 | inf | nan | inf | nan | inf | nan | nan | nan |
| 7 | inf | nan | nan | nan | nan | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.293136 | 0.295731 | 0.287589 | 0.471978 | 0.636502 | 16.0255 | 168.193 | 116.727 |
| 1 | 0.309322 | 52151.2 | 2419.99 | 1.33588e+06 | 61136.3 | 1.71782e+07 | 8.56204e+08 | 5.23585e+08 |
| 2 | 0.312939 | 3.08409e+11 | 4.25722e+09 | 9.81671e+12 | 1.29582e+10 | 1.82842e+13 | 4.30265e+15 | 2.28555e+15 |
| 3 | 0.45337 | 1.82378e+18 | 7.48853e+15 | 7.2137e+19 | 2.74657e+15 | 1.94612e+19 | 2.16219e+22 | 9.97681e+21 |
| 4 | 193007 | 1.0785e+25 | 1.31725e+22 | 5.3009e+26 | 5.8215e+20 | 2.07139e+25 | 1.08656e+29 | 4.35505e+28 |
| 5 | 2.6983e+11 | inf | 2.31706e+28 | inf | 1.2339e+26 | 2.20473e+31 | inf | inf |
| 6 | 3.77233e+17 | nan | inf | nan | 2.61531e+31 | nan | nan | nan |
| 7 | 5.27387e+23 | nan | nan | nan | nan | nan | nan | nan |
predictions_df_80
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.447 | 0.4905 | 0.5387 | 0.5006 | 0.4736 | 0.4716 | 0.3644 | 0.3404 |
| 1 | 0.417 | 0.4656 | 0.4981 | 0.4916 | 0.4119 | 0.3856 | 0.2625 | 0.2213 |
| 2 | 0.4061 | 0.461 | 0.4915 | 0.4891 | 0.3276 | 0.3138 | 0.2377 | 0.2033 |
| 3 | 0.4029 | 0.4598 | 0.4902 | 0.4893 | 0.2801 | 0.2632 | 0.2163 | 0.1949 |
| 4 | 0.3934 | 0.4594 | 0.4899 | 0.4894 | 0.2429 | 0.2289 | 0.1995 | 0.1866 |
| 5 | 0.3803 | 0.4592 | 0.4898 | 0.4896 | 0.2128 | 0.2042 | 0.1852 | 0.18 |
| 6 | 0.3803 | 0.459 | 0.4897 | 0.4897 | 0.1871 | 0.1842 | 0.1677 | 0.1695 |
| 7 | 0.3802 | 0.4587 | 0.4897 | 0.4897 | 0.1709 | 0.1533 | 0.1696 | 0.1583 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.412624 | 0.428987 | 5.25728 | 206.994 | 67.251 | 67323.1 | 1.5699e+06 | 791614 |
| 1 | 61.9679 | 2.98596e+11 | 1.49057e+13 | 1.11191e+16 | 2.92653e+12 | 7.61074e+16 | 3.96073e+19 | 1.50441e+19 |
| 2 | 1.19777e+14 | 1.04419e+25 | 4.61203e+25 | 6.00421e+29 | 1.31475e+23 | 8.62211e+28 | 1.00021e+33 | 2.86661e+32 |
| 3 | 2.34105e+26 | inf | inf | inf | inf | inf | inf | inf |
| 4 | inf | inf | inf | inf | inf | inf | inf | inf |
| 5 | inf | inf | inf | inf | inf | inf | inf | inf |
| 6 | inf | nan | nan | nan | inf | nan | nan | nan |
| 7 | nan | nan | nan | nan | nan | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.297793 | 0.293884 | 0.311141 | 0.568892 | 0.838118 | 26.3605 | 219.248 | 158.456 |
| 1 | 0.401997 | 6491.14 | 32654.4 | 1.99158e+06 | 96557.9 | 2.83942e+07 | 1.11579e+09 | 7.09784e+08 |
| 2 | 120690 | 3.83872e+10 | 5.74402e+10 | 1.46351e+13 | 2.0466e+10 | 3.02223e+13 | 5.60717e+15 | 3.09834e+15 |
| 3 | 1.6873e+11 | 2.27004e+17 | 1.01038e+17 | 1.07544e+20 | 4.33788e+15 | 3.21677e+19 | 2.81775e+22 | 1.35248e+22 |
| 4 | 2.35891e+17 | 1.3424e+24 | 1.77728e+23 | 7.90279e+26 | 9.19438e+20 | 3.42384e+25 | 1.41599e+29 | 5.90381e+28 |
| 5 | 3.29785e+23 | 7.9383e+30 | 3.12627e+29 | inf | 1.9488e+26 | 3.64424e+31 | inf | inf |
| 6 | 4.61052e+29 | nan | nan | nan | 4.13059e+31 | nan | nan | nan |
| 7 | nan | nan | nan | nan | nan | nan | nan | nan |
predictions_df_90
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.3777 | 0.4226 | 0.4579 | 0.4063 | 0.4083 | 0.4039 | 0.3001 | 0.2952 |
| 1 | 0.3566 | 0.3998 | 0.4223 | 0.3962 | 0.3457 | 0.3087 | 0.2162 | 0.176 |
| 2 | 0.3482 | 0.3953 | 0.4162 | 0.3946 | 0.2794 | 0.2469 | 0.1909 | 0.1643 |
| 3 | 0.3416 | 0.3932 | 0.4158 | 0.3943 | 0.2436 | 0.2139 | 0.1811 | 0.1589 |
| 4 | 0.3338 | 0.3928 | 0.4152 | 0.3946 | 0.2056 | 0.1952 | 0.1703 | 0.1559 |
| 5 | 0.3269 | 0.3926 | 0.4149 | 0.3947 | 0.1811 | 0.176 | 0.1617 | 0.1523 |
| 6 | 0.3264 | 0.3926 | 0.4148 | 0.3945 | 0.1619 | 0.163 | 0.1449 | 0.1439 |
| 7 | 0.3264 | 0.3922 | 0.4147 | 0.3946 | 0.1512 | 0.1317 | 0.1495 | 0.1395 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.434935 | 5.47578 | 12.9235 | 665.735 | 129.122 | 121920 | 2.43366e+06 | 1.13164e+06 |
| 1 | 3.09453e+10 | 1.75089e+14 | 3.85059e+13 | 3.58653e+16 | 5.66694e+12 | 1.37862e+17 | 6.14055e+19 | 2.15093e+19 |
| 2 | 6.0483e+22 | 6.12285e+27 | 1.19143e+26 | 1.93668e+30 | 2.54588e+23 | 1.56182e+29 | 1.55069e+33 | 4.09854e+32 |
| 3 | inf | inf | inf | inf | inf | inf | inf | inf |
| 4 | inf | inf | inf | inf | inf | inf | inf | inf |
| 5 | inf | inf | inf | inf | inf | inf | inf | inf |
| 6 | inf | nan | nan | nan | inf | nan | nan | nan |
| 7 | nan | nan | nan | nan | nan | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.302113 | 0.31301 | 0.335783 | 0.784144 | 1.15231 | 44.0107 | 312.26 | 214.05 |
| 1 | 1401.61 | 108044 | 68959.3 | 3.53244e+06 | 156842 | 4.75182e+07 | 1.58767e+09 | 9.57954e+08 |
| 2 | 1.95917e+09 | 6.38934e+11 | 1.213e+11 | 2.59581e+13 | 3.32435e+10 | 5.05775e+13 | 7.97846e+15 | 4.18165e+15 |
| 3 | 2.739e+15 | 3.77836e+18 | 2.1337e+17 | 1.9075e+20 | 7.04613e+15 | 5.38333e+19 | 4.00939e+22 | 1.82536e+22 |
| 4 | 3.82922e+21 | 2.23434e+25 | 3.75321e+23 | 1.40171e+27 | 1.49347e+21 | 5.72987e+25 | 2.01482e+29 | 7.96803e+28 |
| 5 | 5.35341e+27 | inf | 6.60197e+29 | inf | 3.16548e+26 | 6.09871e+31 | inf | inf |
| 6 | inf | nan | nan | nan | 6.70941e+31 | nan | nan | nan |
| 7 | nan | nan | nan | nan | nan | nan | nan | nan |
predictions_df_100
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.3121 | 0.3456 | 0.3711 | 0.3208 | 0.342 | 0.3365 | 0.2589 | 0.251 |
| 1 | 0.2964 | 0.3299 | 0.3434 | 0.3083 | 0.2858 | 0.2578 | 0.1847 | 0.1554 |
| 2 | 0.2886 | 0.3236 | 0.3375 | 0.3077 | 0.2305 | 0.2071 | 0.1673 | 0.1415 |
| 3 | 0.2833 | 0.3225 | 0.3372 | 0.3086 | 0.2034 | 0.1851 | 0.1526 | 0.1391 |
| 4 | 0.2787 | 0.322 | 0.3368 | 0.3085 | 0.1747 | 0.1721 | 0.1449 | 0.1377 |
| 5 | 0.2724 | 0.3218 | 0.3368 | 0.3083 | 0.1512 | 0.1594 | 0.1369 | 0.1346 |
| 6 | 0.2723 | 0.3217 | 0.3365 | 0.3077 | 0.1423 | 0.1478 | 0.1268 | 0.1196 |
| 7 | 0.2723 | 0.3214 | 0.3365 | 0.3077 | 0.1343 | 0.1141 | 0.1275 | 0.1195 |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 34.4267 | 187.285 | 22.7173 | 11225.7 | 550.669 | 244099 | 4.03741e+06 | 1.88882e+06 |
| 1 | 6.61017e+13 | 6.51473e+15 | 6.84727e+13 | 6.05835e+17 | 2.44699e+13 | 2.76091e+17 | 1.01882e+20 | 3.5912e+19 |
| 2 | 1.29197e+26 | 2.27819e+29 | 2.11864e+26 | 3.27143e+31 | 1.09931e+24 | 3.1278e+29 | 2.57286e+33 | 6.84293e+32 |
| 3 | inf | inf | inf | inf | inf | inf | inf | inf |
| 4 | inf | inf | inf | inf | inf | inf | inf | inf |
| 5 | inf | inf | inf | inf | inf | inf | inf | inf |
| 6 | nan | nan | nan | nan | inf | nan | nan | nan |
| 7 | nan | nan | nan | nan | nan | nan | nan | nan |
| normal_dim_iteration256 10_Targets | normal_dim_iteration128 10_Targets | normal_dim_iteration64 10_Targets | normal_dim_iteration32 10_Targets | normal_dim_iteration256 Mnist | normal_dim_iteration128 Mnist | normal_dim_iteration64 Mnist | normal_dim_iteration32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.389266 | 0.519096 | 0.381135 | 2.91465 | 2.28748 | 78.883 | 468.678 | 304.72 |
| 1 | 118716 | 1.31305e+06 | 137995 | 1.92051e+07 | 395787 | 8.51905e+07 | 2.37958e+09 | 1.36068e+09 |
| 2 | 1.65971e+11 | 7.76489e+12 | 2.42741e+11 | 1.41128e+14 | 8.38893e+10 | 9.06751e+13 | 1.1958e+16 | 5.93964e+15 |
| 3 | 2.32034e+17 | 4.59179e+19 | 4.26986e+17 | 1.03706e+21 | 1.77808e+16 | 9.6512e+19 | 6.0092e+22 | 2.59276e+22 |
| 4 | 3.24392e+23 | 2.71537e+26 | 7.51075e+23 | 7.62072e+27 | 3.76873e+21 | 1.02725e+26 | 3.01978e+29 | 1.13178e+29 |
| 5 | 4.53513e+29 | inf | 1.32116e+30 | inf | 7.98803e+26 | 1.09337e+32 | inf | inf |
| 6 | nan | nan | nan | nan | 1.69311e+32 | nan | nan | nan |
| 7 | nan | nan | nan | nan | nan | nan | nan | nan |
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)